The repository describes the feature extraction methods for Audio signals.

• OpenLSR: OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition.
• VoxForge: VoxForge is now mirroring the LT and the Teleccoperation group Open Speech Data Corpus for German with 35 hours of speech from about 180 speakers.
• TIMIT: The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus.
• Mozilla Speech: Mozilla Releases the world's Second Largest Public Voice Data Set on Nov 29th, 2017.
• Open Data for Deep Learning

• feature_extraction_functions.py: a set of feature extraction functions from RDShi-SpeakerCount.
• MFCC: Mel-frequency cepstral coefficients calculation.
  • MFCC.py, MFCCTest.py: Compute the MFCC feature.
  • FeatureExtraction.ipynb: Speech preprocessing, including loading data, pre-emphasis, framing, window, Fourier-transform, power spectrum, filter banks, mfccs and mean normalization.
• Volume: volume calculation.
  
• ZeroCR: Zero-Crossing Rate calculation.
  
• Pitch: Pitch calculation and pitch tracking.
  
• Timbre: spectrogram drawing.
  
• VAD: EPD (End-Point Detection), or Speech Detection, or VAD(Voice Activity Detection).
  

Anaconda (Python)

• http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
• https://github.com/wiseman/py-webrtcvad
• https://github.com/jameslyons/python_speech_features
• https://github.com/ZhihaoDU/speech_feature_extractor
• http://ibillxia.github.io/blog/archives/
• http://stevemorphet.weebly.com/speech-and-audio-processing
• MFCC
  • http://blog.csdn.net/zouxy09/article/details/9156785
  • http://blog.csdn.net/xmdxcsj/article/details/51228791
  • http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
• Git tutorial
  • https://jingyan.baidu.com/article/2fb0ba4091a21c00f2ec5fbf.html
  • https://jingyan.baidu.com/article/fec4bce2285b56f2618d8bdc.html